MechE PhD Prospectus Defense: Bee Vang


MechE PhD Prospectus Defense: Bee Vang



ABSTRACT: The attitude (or orientation) of an object is often crucial in its ability to perform a task, whether the task is driving a car, flying an aircraft, orientating a satellite, or capturing the perfect moment with a camera. While it is intuitive for humans to move (or control) the objects to the proper attitude to perform these tasks, it is not trivial for autonomous systems. In traditional control approaches, the attitude is often parameterized by Euler angles or quaternions which exhibit problems such as gimbal lock or ambiguity in representation, respectively. This means that controllers using these parameterizations cannot achieve global stability. More recent works have achieved global stability by working directly on the configuration manifold, but are generally complex and have many parameters to tune.

In this work, we present a simple geometric attitude controller with intuitive tuning parameters, showing that it is locally exponentially stable and almost globally asymptotically stable; the exponential convergence region is much larger than existing non-hybrid geometric controllers (and covers almost the entire rotation space). The controller’s stability and convergence rate are proved using contraction analysis combined with optimization. The key in this combination is that the contraction metric is a linear matrix inequality with a special structure stemming from the configuration manifold SO(3), and we show that the controller’s parameters can be automatically tuned. On the way, we generalized results on Riemannian metrics for tangent bundles to allow for a larger set of solutions.

In future work, we plan to improve the simple geometric attitude controller by introducing a “feed-forward” term. The idea being that if the local controller converges exponentially to a reference trajectory that is at the same time converging exponentially to a desired attitude, then the whole system is globally exponentially stable. In addition, we plan to extend our framework to use the stabilizing metric to implicitly define a controller that is point-wise (i.e., for a single state and time) optimal; this extension is inspired by, but not a straightforward application of, recent results on Control Lyapunov Functions and Control Barrier Functions.

COMMITTEE: ADVISOR Professor Roberto Tron, ME/SE; Professor Calin Belta, ME/SE/ECE; Professor John Baillieul, ME/SE/ECE; Professor Sean Andersson, ME/SE


10:00am on Wednesday, November 6th 2019

End Time



110 Cummington Mall, Room 245

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